Abstract
Purpose: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data. Methods: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time. Results: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients. Conclusions: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease.
| Original language | English |
|---|---|
| Journal | European Journal of Nuclear Medicine and Molecular Imaging |
| DOIs | |
| Publication status | Accepted/In press - Jan 1 2018 |
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Keywords
- Alzheimer disease
- Classification and prediction
- Discriminant analysis
- FDG-PET
- MCI due to AD
- Neurodegenerative disorders
- Neuroimage classification
- Support vector machine
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
Cite this
Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease. / for the Alzheimer’s Disease Neuroimaging Initiative.
In: European Journal of Nuclear Medicine and Molecular Imaging, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease
AU - for the Alzheimer’s Disease Neuroimaging Initiative
AU - De Carli, Fabrizio
AU - Nobili, Flavio
AU - Pagani, Marco
AU - Bauckneht, Matteo
AU - Massa, Federico
AU - Grazzini, Matteo
AU - Jonsson, Cathrine
AU - Peira, Enrico
AU - Morbelli, Silvia
AU - Arnaldi, Dario
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Purpose: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data. Methods: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time. Results: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients. Conclusions: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease.
AB - Purpose: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data. Methods: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time. Results: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients. Conclusions: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease.
KW - Alzheimer disease
KW - Classification and prediction
KW - Discriminant analysis
KW - FDG-PET
KW - MCI due to AD
KW - Neurodegenerative disorders
KW - Neuroimage classification
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85055986240&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055986240&partnerID=8YFLogxK
U2 - 10.1007/s00259-018-4197-7
DO - 10.1007/s00259-018-4197-7
M3 - Article
AN - SCOPUS:85055986240
JO - European Journal of Pediatrics
JF - European Journal of Pediatrics
SN - 0340-6199
ER -